A METHOD FOR IMPROVING THE PROFESSIONAL PERFORMANCE AND RELIABILITY OF PERSONS DRIVING HIGH-SPEED VEHICLES.

Recently, due to the emergence of a variety of modifications of air, land, water vehicles and an increase in their speed and maneuverability, the number of people with severe manifestations of motion sickness has also increased. The relevance of this problem is dictated by the fact that, despite significant achievements in the field of preventive medicine, a significant number of people prone to motion sickness have been observed to date. Thus, among persons using land modes of transport, the percentage of sick people reaches 15.0%, air modes 20.0%, while using water modes of transport, the number of sick people reaches 30.0%. The significance of this problem is dictated by the fact that the psycho-physiological capabilities of our body do not keep pace with the rapidly increasing speed-maneuvering characteristics of vehicles.

Outracing champion Gran Turismo drivers with deep reinforcement learning.

Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block opponents while operating their vehicles at their traction limits1. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the non-linear control challenges of real race cars while also encapsulating the complex multi-agent interactions. Here we describe how we trained agents for Gran Turismo that can compete with the world's best e-sports drivers. We combine state-of-the-art, model-free, deep reinforcement learning algorithms with mixed-scenario training to learn an integrated control policy that combines exceptional speed with impressive tactics. In addition, we construct a reward function that enables the agent to be competitive while adhering to racing's important, but under-specified, sportsmanship rules. We demonstrate the capabilities of our agent, Gran Turismo Sophy, by winning a head-to-head competition against four of the world's best Gran Turismo drivers. By describing how we trained championship-level racers, we demonstrate the possibilities and challenges of using these techniques to control complex dynamical systems in domains where agents must respect imprecisely defined human norms.

An optimized location service for the fifth generation VANETs inspired by traffic lights.

Vehicular ad-hoc networks (VANETs) address a steadily expanding demand, particularly for public emergency applications. Real-time localization of destination vehicles is important for determining the route to deliver messages. Existing location administration services in VANETs are classified as flooding-based, flat-based, and geographic-based location services. Existing localization techniques suffer from network disconnection and overloading because of 5G VANET topology changes. 5G VANETs have low delay and support time-sensitive applications. A traffic light-inspired location service (TLILS) is proposed to manage localization inspired by traffic lights. The proposed optimized localization service uses roadside units (RSUs) as location servers. RSUs with the maximum traffic weight metrics were chosen. Traffic weight metrics are based on speed of vehicles, connection time and density of neighboring vehicles. The proposed TLILS outperforms both Name-ID Hybrid Routing (NIHR) and Zoom-Out Geographic Location Service (ZGLS) for packet delivery ratio (PDR) and delay. TLILSs guarantee the highest PDR (0.96) and the shortest end-to-end delay (0.001 s) over NIHR and ZGLS.

Diesel matters: accelerating the light diesel vehicle endgame in Aotearoa New Zealand.

Air pollution from diesel-powered vehicles is likely to be contributing substantial harm to health in Aotea-roa New Zealand, as well as making it harder for this country to meet its international climate change commitments. There are a lack of controls and outdated standards applied to diesel vehicles in New Zea-land, and there is scope to extend the monitoring of emissions. A comprehensive list of interventions that would assist with the phase-out of light diesel vehicles and reducing their emissions during the transition has been compiled. This list includes regulatory interventions such as bringing forward the year in which the Climate Change Commission proposes to ban imports of internal combustion light vehicles (ie, from 2035 to 2025). Also detailed are fiscal measures (incentives and disincentives) and improvements to in-formation for consumers at point-of-sale.

Modeling learner-controlled mental model learning processes by a second-order adaptive network model.

Learning knowledge or skills usually is considered to be based on the formation of an adequate internal mental model as a specific type of mental network. The learning process for such a mental model conceptualised as a mental network, is a form of (first-order) mental network adaptation. Such learning often integrates learning by observation and learning by instruction. For an effective learning process, an appropriate timing of these different elements is crucial. By controlling the timing of them, the mental network adaptation process becomes adaptive itself, which is called second-order mental network adaptation. In this paper, a second-order adaptive mental network model is proposed addressing this. The first-order adaptation process models the learning process of mental models and the second-order adaptation process controls the timing of the elements of this learning process. It is illustrated by a case study for the learner-controlled mental model learning in the context of driving a car. Here the learner is in control of the integration of learning by observation and learning by instruction.

Research on the speed thresholds of trucks in a sharp turn based on dynamic rollover risk levels.

Truck rollover is a problem that seriously endangers the safety of human life. Under special conditions, when the driver takes a sharp turn, the truck is most prone to rollover. Speed seriously affects the driving stability of the truck in a sharp turn, but the calculation of the safe speed is not accurate enough at present. The aim of this paper is to develop a more accurate safe speed calculation method to avoid the truck rollover in a sharp turn. Firstly, the calculation formula of the rollover threshold was derived based on a theoretical model, then, the simulation tests were carried out. We selected a 4-axle truck with a total weight of 30t as the subject, simulated the dynamic process of the truck rollover in a sharp turn with TruckSim, evaluated the dynamic rollover risk levels of the truck during this process, and verified the accuracy of the simulation results by results of the theoretical model. Finally, by analyzing the steering principle of the vehicle, the safe speed threshold and the limit speed threshold of the truck in a sharp turn were calculated according to the lateral acceleration corresponding to the rollover risk levels. The results show that no matter what the loading condition of the truck is, when the rollover margin is reduced to about 0.15g, the truck just reaches the risk level of critical rollover; the result provides an accurate algorithm for speed thresholds of the truck when turning radius is less than 250 m. The research provides a calculation method for safe speed of trucks from a dynamic perspective. The research results can be applied to the speed warning system of trucks, which can make drivers better control the rollover risk of trucks in the process of driving and improve driving safety.

Compliance, practices, and attitudes towards VTIs (Vehicle Technical Inspections) in Spain: What prevents Spanish drivers from checking up their cars?

OBJECTIVE: Mechanical conditions of vehicles may play a determinant role in driving safety, the reason why vehicle periodical technical inspections (VTIs) are mandatory in many countries. However, the high number of drivers sanctioned for not complying with this regulation is surprisingly high, and there is not much evidence on what kind(s) of motives may explain this concerning panorama. This study aimed to identify the aspects that modulate the relationship between complying (or not) with VTI's standards in a nationwide sample of Spanish drivers. The study design also addressed the drivers' awareness regarding different risky behaviors while driving, depending on their sex and their crash record. METHODS: 1,100 Spanish drivers completed a survey on the aforementioned issues. An analysis of variance (ANOVA) with Bonferroni post-hoc adjustment was conducted to assess significant differences (p<0.05) in the study variables. RESULTS: Most of the surveyed drivers (99.18%) reported that they always comply with VTI's requirements. The main reasons to comply were related to compliance with traffic regulation and fear of penalties, while the reasons attributed to its incompliance are, instead, stated as involuntary. CONCLUSION: The findings of this study support the idea that more actions are needed to increase drivers' awareness of the relevance of VTIs for road safety, as well as warning them about the dangers of neglecting vehicle checking beyond merely punishing measures. For this reason and given the greater prevalence of the issue among younger segments of the driving population, it is suggested that more emphasis on the matter could be made during novice driver's training.

Post-trip safety interventions: State-of-the-art, challenges, and practical implications.

INTRODUCTION: Currently, risky driving behaviour is a major contributor to road crashes and as a result, wide array of tools have been developed in order to record and improve driving behaviour. Within that group of tools, interventions have been indicated to significantly enhance driving behaviour and road safety. This study critically reviews monitoring technologies that provide post-trip interventions, such as retrospective visual feedback, gamification, rewards or penalties, in order to inform an appropriate driver mentoring strategy delivered after each trip. METHOD: The work presented here is part of the European Commission H2020 i-DREAMS project. The reviewed platform characteristics were obtained through commercially available solutions as well as a comprehensive literature search in popular scientific databases, such as Scopus and Google Scholar. Focus was given on state-of-the-art-technologies for post-trip interventions utilized in four different transport modes (i.e. car, truck, bus and rail) associated with risk prevention and mitigation. RESULTS: The synthesized results revealed that smartphone applications and web-based platforms are the most accepted, frequently and easiest to use tools in cars, buses and trucks across all papers considered, while limited evidence of post-trip interventions in -rail was found. The majority of smartphone applications detected mobile phone use and harsh events and provided individual performance scores, while in-vehicle systems provided delayed visual reports through a web-based platform. CONCLUSIONS: Gamification and appropriate rewards appeared to be effective solutions, as it was found that they keep drivers motivated in improving their driving skills, but it was clear that these cannot be performed in isolation and a combination with other strategies (i.e. driver coaching and support) might be beneficial. Nevertheless, as there is no holistic and cross-modal post-trip intervention solution developed in real-world environments, challenges associated with post-trip feedback provision and suggestions on practical implementation are also provided.

Is a flick-through enough? A content analysis of Advanced Driver Assistance Systems (ADAS) user manuals.

Advanced Driver Assistance Systems (ADAS) are being developed and installed in increasing numbers. Some of the most popular ADAS include blind spot monitoring and cruise control which are fitted in the majority of new vehicles sold in high-income countries. With more drivers having access to these technologies, it is imperative to develop policy and strategies to guarantee the safe uptake of ADAS. One key issue is that ADAS education has been primarily centred on the user manual which are not widely utilised. Moreover, it is unclear if user manuals are an adequate source of education in terms of content and readability. To address this research gap, a content analysis was used to assess the differences in ADAS-related content and readability among the manuals of the highest selling vehicles in Australia. The qualitative findings showed that there are seven themes in the user manuals: differences between driving with and without ADAS, familiarisation requirements, operational limits of the ADAS, potential ADAS errors, behaviour adaptation warnings, confusion warnings, and malfunction warnings. The quantitative analysis found that some of the manuals require several years of education above the recommended for a universal audience (>8 years) to be understood. Additionally, there is a notable number of text diversions and infographics which could make comprehension of the user manual difficult. This investigation shows that there is a lack of standardisation of ADAS user manuals (in both content and delivery of information) which requires regulatory oversight. Driver ADAS education needs to be prioritised by policymakers and practitioners as smart technology continues to increase across the transport system. It seems that current strategies based on user manuals are insufficient to achieve successful adoption and safe use of these technologies.

Riding with an impaired driver and driving while impaired among adolescents: Longitudinal trajectories and their characteristics.

PURPOSE: The purpose of this study was to identify and characterize trajectory classes of adolescents who ride with an impaired driver (RWI) and drive while impaired (DWI). METHODS: We analyzed all 7 annual assessments (Waves W1-W7) of the NEXT Generation Health Study, a nationally representative longitudinal study starting with 10th grade (2009-2010 school year). Using all 7 waves, latent class analysis was used to identify trajectory classes with dichotomized RWI (last 12 months) and DWI (last 30 days; once or more = 1 vs. none = 0). Covariates were race/ethnicity, sex, parent education, urbanicity, and family affluence. RESULTS: Four RWI trajectories and 4 DWI trajectories were identified: abstainer, escalator, decliner, and persister. For RWI and DWI trajectories respectively, 45.0% (n = 647) and 76.2% (n = 1,657) were abstainers, 15.6% (n = 226) and 14.2% (n = 337) were escalators, 25.0% (n = 352) and 5.4% (n = 99) were decliners, and 14.4% (n = 197) and 3.8% (n = 83) persisters. Race/ethnicity (χ2 = 23.93, P = .004) was significantly associated with the RWI trajectory classes. Race/ethnicity (χ2 = 20.55, P = .02), sex (χ2 = 13.89, P = .003), parent highest education (χ2 = 12.49, P = .05), urbanicity (χ2 = 9.66, P = .02), and family affluence (χ2 = 12.88, P = .05) were significantly associated with DWI trajectory classes. CONCLUSIONS: Among adolescents transitioning into emerging adulthood, race/ethnicity is a common factor associated with RWI and DWI longitudinal trajectories. Our results suggest that adolescent RWI and DWI are complex behaviors warranting further detailed investigation of the respective trajectory classes. Our study findings can inform the tailoring of prevention and intervention efforts aimed at preventing illness/injury and preserving future opportunities for adolescents to thrive in emerging adulthood.

A safety score for the assessment of driving style.

OBJECTIVE: Road traffic laws explicitly refer to a safe and cautious driving style as a means of ensuring safety. For automated vehicles to adhere to these laws, objective measurements of safe and cautious behavior in normal driving conditions are required. This paper describes the conception, implementation and initial testing of an objective scoring system that assigns safety indexes to observed driving style, and aggregates them to provide an overall safety score for a given driving session. METHODS: The safety score was developed by matching safety indexes with maneuver-based parameter ranges processed from an existing highway traffic data set with a newly developed algorithm. The concept stands on the idea that safety, rather than suddenly changing from a safe to an unsafe condition at a certain parameter value, can be better modeled as a continuum of values that consider the safety margins available for interactions among multiple vehicles and that depend on present traffic conditions. A sensitivity test of the developed safety score was conducted by comparing the results of applying the algorithm to two drivers in a simulator who were instructed to drive normally and risky, respectively. RESULTS: The evaluation of normal driving statistics provided suitable ranges for safety parameters like vehicle distances, time headways, and time to collision based on real traffic data. The sensitivity test provided preliminary evidence that the scoring method can discriminate between safe and risky drivers based on their driving style. In contrast to previous approaches, collision situations are not needed for this assessment. CONCLUSIONS: The developed safety score shows potential for assessing the level of safety of automated vehicle (AV) behavior in traffic, including AV ability to avoid exposure to collision-prone situations. Occasional bad scores may occur even for good drivers or autonomously driving vehicles. However, if the safety index becomes low during a significant part of a driving session, due to frequent or harsh safety margin violations, the corresponding driving style should not be accepted for driving in real traffic.

Drug use and pattern of injuries sustained by drivers involved in road traffic crashes.

OBJECTIVE: Road traffic crashes are one of the global public health concerns and remain at high priority in many countries. Driving under the influence of drugs increases the risk of crashes through altering the driver's mental state and reactions. This study was conducted to determine the relationship between driving pattern and substance abuse among drivers in Kerman, a city in Iran, in order to enable policy makers to make the necessary decisions in planning and executing guidelines. METHODS: The population of this descriptive study was drivers involved in road traffic crashes admitted to the emergency department of Shahid Bahonar university hospital in summer 2019. After obtaining demographic information, type of vehicle, type of collision and pattern of serious injury, 222 eligible drivers were tested for tramadol, cannabis, amphetamine, methamphetamine, morphine and methadone using one step urine test strips. Chi-square test, Fisher's exact test, Whitney-Mann and Kruskal-Wallis tests and one-way Anova test was performed using SPSS version 22. RESULTS: The statistics showed that most drivers were male (90.5%), married (63.5%), age group (18-30) and had positive urine test (76.6%). In addition to uniqueness of dual-drug detection among male drivers (7.7%), the most common substances detected were methadone and morphine with 34.7% and 27.5% respectively. The most common injuries were lower limb and hip injuries mostly among motorcyclists. The results indicated that characteristics of being under 30's, married, school dropout, self-employed and motorcyclist had significant relationship with substance use. CONCLUSIONS: Substance use, especially methadone, has undoubtedly a significant role in both road traffic crashes and resulted injuries. The high rate of injuries on drivers influenced by methadone in traffic crashes needs to be screened and prevented. It is recommended to not only authoritatively deal with the excessive supply of methadone in the community but also restricting the driving of people receiving methadone treatment.

Associating ridesourcing with road safety outcomes: Insights from Austin, Texas.

Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nations sustainable development goals and worldwide vision zero efforts. The advent of transportation network companies and ridesourcing expands mobility options in cities and may impact road safety outcomes. We analyze the effects of ridesourcing use on road crashes, injuries, fatalities, and driving while intoxicated (DWI) offenses in Travis County, Texas. Our approach leverages real-time ridesourcing volume to explain variation in road safety outcomes. Spatial panel data models with fixed-effects are deployed to examine whether the use of ridesourcing is significantly associated with road crashes and other safety metrics. Our results suggest that for a 10% increase in ridesourcing trips, we expect a 0.12% decrease in road crashes, a 0.25% decrease in road injuries, and a 0.36% decrease in DWI offenses in Travis County. On the other hand, ridesourcing use is not significantly associated with road fatalities. This study augments existing work because it moves beyond binary indicators of ridesourcing availability and analyzes crash and ridesourcing trips patterns within an urbanized area rather than their metropolitan-level variation. Contributions include developing a data-rich approach for assessing the impacts of ridesourcing use on the transportation system's safety, which may serve as a template for future analyses for other cities. Our findings provide feedback to policymakers by clarifying associations between ridesourcing use and traffic safety and uncover the potential to achieve safer mobility systems with transportation network companies.

Injury crashes and the relationship with disease causing excessive daytime sleepiness.

OBJECTIVE: The objective of this study was to understand the relationship between some of the most common diseases that are known to contribute to excessive daytime sleepiness (EDS) and traffic injury crashes. Specific focus was on the relationship between disease and crash type (single-vehicle or multiple-vehicle crash) and between disease and injury severity. METHODS: This registry-based study considered all passenger car drivers involved in a crash in Sweden between 2011 and 2016 who were 40 years or older at the time of the crash (n = 54,090). For each crash-involved driver, selected medical diagnoses registered from 1997 until the day before the crash were extracted from the National Patient Register. The drivers were assigned to 1 of 4 groups, depending on prior diseases: sleep apnea (SA; group 1, n = 2,165), sleep disorders (group 2, n = 724), Parkinson's or epilepsy (group 3, n = 645) and a reference group (group 4, n = 50,556). Logistic regression analysis compared single-vehicle crashes with multiple-vehicle crashes and moderately/severely injured drivers with slightly/uninjured drivers. RESULTS: Drivers with EDS-related diseases (groups 1-3) had higher probability of a single-vehicle crash than a multiple-vehicle crash compared to the reference group. The most sizeable effect was found for Parkinson's/epilepsy with an odds ratio (OR) of 2.5 (confidence interval [CI], 2.1-3.0). For multiple-vehicle crashes, the probability of a moderate/severe injury was higher for drivers with other sleep disorders (OR = 1.5; CI, 1.0-2.2) and Parkinson's/epilepsy (OR = 1.6; CI, 1.1-2.3) compared to the reference group. CONCLUSIONS: This study has made first steps toward understanding the relationship between some of the most common diseases that are known to contribute to EDS and crashes. Having Parkinson's/epilepsy, in particular, elevated the probability of a single-vehicle crash compared to a multiple-vehicle crash. A single-vehicle crash was seen as indicative of causing a crash; thus, having Parkinson's/epilepsy could be interpreted as a risk factor for crash involvement. Having Parkinson's/epilepsy, as well as other sleep disorders, was also related to more severe outcomes in multiple-vehicle crashes, given that a crash occurred. This was not identified in single-vehicle crashes.

Human error in motorcycle crashes: A methodology based on in-depth data to identify the skills needed and support training interventions for safe riding.

OBJECTIVE: Human error by either rider or other vehicle driver is the primary contributing factor in crashes involving powered-two-wheelers. A human-factor-based crash analysis methodology is key to enhancing the road safety effectiveness of rider training interventions. Our aim is to define a methodology that uses in-depth data to identify the skills needed by riders in the highest risk crash configurations to reduce casualty rates. METHODS: The methodology is illustrated through a case study using in-depth data of 803 powered-two-wheeler crashes. Seven types of high-risk crash configuration based on pre-crash trajectories of the road-users involved were considered to investigate the human errors as crash contributors. Primary crash contributing factor, evasive maneuvers performed, horizontal roadway alignment and speed-related factors were identified, along with the most frequent crash configurations and those with the greatest risk of severe injury. RESULTS: Straight Crossing Path/Lateral Direction was the most frequent crash configuration and Turn Across Path/Opposing Direction was identified as that with the highest risk of serious injury. Multi-vehicle crashes cannot be considered as a homogenous category of crashes to which the same human failure is attributed, as different interactions between motorcyclists and other road users are associated with both different types of human error and different rider reactions. Human error in multiple-vehicle crashes differed between those configurations related to crossroads and those related to rear-end and head-end crashes. Both single-vehicle crashes and multi-vehicle head-on crashes frequently occur along curves. The involved collision avoidance maneuvers of the riders differed significantly among the highest risk crash configurations. The most relevant lack of skills are identified and linked to their most representative context. In most cases a combination of different skills was required simultaneously to avoid the crash. CONCLUSIONS: The results contribute to understand the motorcyclists' responses in high-risk crash scenarios. The findings underline the need to group accident cases, beyond the usual single-vehicle versus multi-vehicle collision approach. The different interactions with other road users should be considered to identify the competencies of the motorcyclists needed to reduce crash risks. Our methodology can be applied to increase the motorcyclists' safety by supporting preventive actions based on riders' training and eventually ADAS design.

Quantifying the automated vehicle safety performance: A scoping review of the literature, evaluation of methods, and directions for future research.

Vehicle automation safety must be evaluated not only for market success but also for more informed decision-making about Automated Vehicles' (AVs) deployment and supporting policies and regulations to govern AVs' unintended consequences. This study is designed to identify the AV safety quantification studies, evaluate the quantification approaches used in the literature, and uncover the gaps and challenges in AV safety evaluation. We employed a scoping review methodology to identify the approaches used in the literature to quantify AV safety. After screening and reviewing the literature, six approaches were identified: target crash population, traffic simulation, driving simulator, road test data analysis, system failure risk assessment, and safety effectiveness estimation. We ran two evaluations on the identified approaches. First, we investigated each approach in terms of its input (required data, assumptions, etc.), output (safety evaluation metrics), and application (to estimate AVs' safety implications at the vehicle, transportation system, and society levels). Second, we qualitatively compared them in terms of three criteria: availability of input data, suitability for evaluating different automation levels, and reliability of estimations. This review identifies four challenges in AV safety evaluation: (a) shortcomings in AV safety evaluation approaches, (b) uncertainties in AV implementations and their impacts on AV safety, (c) potential riskier behavior of AV passengers as well as other road users, and (d) emerging safety issues related to AV implementations. This review is expected to help researchers and rulemakers to choose the most appropriate quantification method based on their goals and study limitations. Future research is required to address the identified challenges in AV safety evaluation.

Comparing oculomotor efficiency and visual attention between drivers and non-drivers through the Adult Developmental Eye Movement (ADEM) test: A visual-verbal test.

OBJECTIVE: The objective of this study was to assess and compare drivers' and non-drivers' outcomes in the Adult Developmental Eye Movement test (ADEM), a visual-verbal test that measures the time needed to read series of numbers in both a vertical and horizontal reading pattern. A set of driving parameters (i.e., experience, risk exposure, and day and night perceived difficulty) and demographic variables (i.e., age, gender, and academic level) were considered as potential predictors of the test performance. METHODS: For this cross-sectional study, 302 healthy subjects (age range 20 to 86 years old) completed a self-reported questionnaire aimed at retrieving data on the independent variables, and underwent the ADEM in order to obtain the dependent outcomes. 214 (70.9%) of the participants were drivers. Non-parametric analyses and multilevel linear regression were used to assess differences between the variables and a prediction model. Also, some correlations were evaluated through the Spearman test. RESULTS: Drivers showed significantly better test performance than non-drivers. The age, driving experience, and perceived difficulty in driving at night were obtained as potential predictors of the test performance with the applied linear regression model. CONCLUSION: The ADEM may be a practical, non-expensive, easy-to-apply tool in the assessment of drivers, useful for obtaining or renewing the driving license. This test may help in the detection of impairments in the saccadic efficiency that could have a detrimental effect on the driving performance.

Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles.

The introduction of Automated Vehicles (AVs) into the transportation network is expected to improve system performance, but the impacts of AVs in mixed traffic streams have not been clearly studied. As AV's market penetration increases, the interactions between conventional vehicles and AVs are inevitable but by no means clear. This study aims to create new knowledge by quantifying the behavioral changes caused when conventional human-driven vehicles follow AVs and investigating the impact of these changes (if any) on safety and the environment. This study analyzes data obtained from a field experiment by Texas A&M University to evaluate the effects of AVs on the behavior of a following human-driver. The dataset is comprised of nine drivers that attempted to follow 5 speed-profiles, with two scenarios per profile. In scenario one, a human-driven vehicle follows an AV that implements a human driver speed profile (base). In scenario two, the human-driven vehicle follows an AV that executes an AV speed profile. In order to evaluate safety, these scenarios are compared using time-to-collision (TTC) and several other driving volatility measures. Likewise, fuel consumption and emissions are used to investigate environmental impacts. Overall, the results show that AVs in mixed traffic streams can induce behavioral changes in conventional vehicle drivers, with some beneficial effects on safety and the environment. On average, a driver that follows an AV exhibits lower driving volatility in terms of speed and acceleration, which represents more stable traffic flow behavior and lower crash risk. The analysis showed a remarkable improvement in TTC as a result of the notably better speed adjustments of the following vehicle (i.e., lower differences in speeds between the lead and following vehicles) in the second scenario. Furthermore, human-driven vehicles were found to consume less fuel and produce fewer emissions on average when following an AV.

At what stages of licensing do graduated driver licensing systems reduce crashes? Example from Queensland, Australia.

Evaluations of new graduated licensing systems (GLS) commonly examine pre-post young driver crash rates relative to another driver group. This comparison approach is important to account for other influences on crashes over time, but has limited ability to determine which GLS components are most effective and at what stage during the licensing process. We previously identified declines in young driver crashes in Queensland, Australia, following introduction of a new GLS in 2007. The objective of the current research was to conduct complementary modelling to identify at what points through the licensing process had particular GLS policies contributed to reductions. Crash trends were explored for learner and provisional drivers under the new GLS versus previous system for three time periods relative to the month of acquiring a provisional licence: the preceding learner period, the first month of provisional licensure (when crashes typically peak), and the overall provisional period. Interrupted time series analyses were conducted for the log ratio of crashes per 10,000 licensed (learner and provisional) drivers with the total number of licensed drivers as an offset. The greatest declines were found in the first month of licensure, with indications that a longer learner period, higher supervised driving hours, and a new provisional night-passenger restriction were key contributors to provisional crash reductions. There was also some indication that a restriction on all phone use reduced crashes during the learner period. We conclude that time series analysis focusing on licensing stage, rather than calendar time only, offers a complementary approach to analysing GLS effectiveness by better identifying where and how changes impact crashes.

Assessing fitness to drive in older adults: Validation and extension of an economical screening tool.

OBJECTIVES: Due to demographic change, the number of older drivers with impaired driving skills will increase in the next decades. The current study aimed at the validation and extension of the screening tool Safety Advice For Elderly drivers (SAFE) that allows a cost-efficient assessment of driving-related risk factors in older drivers. METHOD: Seventy-four older drivers aged ≥65 years (M = 77 years) recruited from the general population were included in this prospective observational study. Receiver operating characteristic curve (ROC) and hierarchical logistic regression analyses were utilized to examine whether the SAFE and further evidence-based driving-related factors may allow the differentiation between fit and unfit older drivers assessed in standardized on-road driving assessments. RESULTS: ROC analyses revealed significant diagnostic accuracy of the number of SAFE risk factors in differentiating between fit and unfit older drivers (AUC = 0.71). A stepwise logistic regression model revealed that adding further evidence-based risk factors into the SAFE clearly improved diagnostic accuracy (AUC = 0.85). DISCUSSION: The current study shows that the risk assessed by the SAFE predicts on-road driving fitness in older adults. However, the results also suggest a need for a modification of the SAFE by the inclusion of additional evidence-based risk factors. With sensitivity and specificity scores of about 90 % and 75 %, this modified version may be more suitable for clinical use.